Prognostic Value of the Intratumoral Lymphocyte to Monocyte Ratio and M0 Macrophage Enrichment in the Melanoma Immune Microenvironment

Background: Cutaneous melanoma (SKCM) is characterized by significant heterogeneity in its molecular, genomic, and immunologic characteristics. Methods: Whole transcriptome RNAseq data from The Cancer Genome Atlas of SKCM (n=328) was utilized. The immune microenvironment was characterized using CIBERSORTx to identify immune cell type composition. Unsupervised hierarchical clustering was performed based on immune cell type content. Samples were separated into those obtained from the primary tumor site and r egional skin or soft tissue (locoregional), or distant metastasis and regional lymph node (metastatic). Analysis of overall survival (OS) was performed using Kaplan-Meier estimates and Cox-regression multivariable analyses. Results: Four immune clusters were identified, largely defined by lymphocyte:monocyte (L:M) ratio, monocyte-enrichment, and M0-macrophage-enrichment (L:MLow, MonocyteHigh, M0High; L:MLow, MonocyteMid, M0Low; L:MMid, MonocyteLow, M0Low; L:MHigh, Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 11 September 2020 doi:10.20944/preprints202009.0239.v1 © 2020 by the author(s). Distributed under a Creative Commons CC BY license.

score (a surrogate for distribution and density of lymphocytic infiltrate in the tumor sample), timing and dose of therapies, including chemotherapies, immunotherapies, and radiation was obtained from the 'TCGAbiolinks' web tool (R Foundation for Statistical Computing Version 3.6.2, Vienna, Austria). Patients still alive were censored at the time of last follow up. Pat ients were separated into two groups for Kaplan-Meier analysis: those in whom the sample was taken from a regional lymph node or distant metastasis site (metastasis), compared with those for whom the tumor was taken from the primary site or regional skin or soft tissue (locoregional). Each endpoint was assessed using the Kaplan-Meier method, and survival curves were compared using the Mantel-Cox log-rank test. Survival analysis was carried out using the survminer R package (R Foundation for Statistical Computing Version 3.6.2, Vienna, Austria). Log-rank p-value and risk tables are displayed on each chart.

Retrieving Raw Count Data
The "TCGAbiolinks" package was downloaded from Bioconductor [29][30][31]. The GDC query function was used to retrieve Illumina HiSeq RNA data from locoregional tumors in the TCGA SKCM dataset and the data was downloaded using the GDCdownload function. Raw count data was normalized, and low count genes w ere filtered according to the default 25% quantile across all samples. The table of normalized and filtered raw count data was extracted for use in downstream analysis.

Statistical Analysis
Clinical, pathologic and molecular characteristics were compared between clusters using statistical methods in R using a chi-square test. The Kaplan Meier method was used to compare endpoints across clusters with the log-rank test. To determine the influence of immunologic and clinicopathologic covariates on OS, a multivariable Cox-regression analysis was performed.
Statistical significance was set at p<0.05.

Prediction of Immunotherapy Response -TIDE
Understanding the relevance of the immune composition of tumors to predicting response to immune checkpoint blockade (ICB) is essential. However, most publicly available transcriptome data does not have corresponding ICB treatment data. To overcome this limitation, the Tumor Immune Dysfunction and Exclusion (TIDE) tool was utilized. TIDE is a web -based tool that uses a gene expression signature to predict response to ICB [32]. Z-score transformed whole transcriptome (RSEM) data was downloaded from the cBio Portal and input into the tool. TIDE performance has been validated on melanoma and NSCLC datasets, making it an ideal choice for our study. Output included response prediction, with lower TIDE score corresponding to better immunotherapy response, and other gene signatures associated with immune dysfunction, including interferon gamma response (IFNG), microsatellite instability (MSI), CD274 (T regulatory cell marker), and T cell dysfunction.  Figure 1A. The absolute immune cell infiltration in each cluster was determined by the CIBERSORT absolute immune score output and displayed in Figure 1B. Pairwise t-tests were conducted to confirm significant differences between cell types between clusters. All clusters demonstrated significantly different levels of each type of immune cell infiltrate (p < 0.05). There were no significant differences between median age, gender, site of primary tumor, specimen site, or median follow up between clusters.

TIME Clustering and Baseline Demographics:
Full demographic information can be found in Table 1.

TIME Cluster Prognostic Effect
The median follow -up time for all patients was 40  primary tumor or regional skin or soft tissue) or metastatic (distant metas tasis or regional lymph node) collection site to understand whether the TIME of the locoregional site might guide tumor behavior. There were no significant differences between clusters in the metastatic site cohort

Multivariable Survival Analysis
After establishing that M0-macrophage-enriched, L:M low SKCM in the locoregional tumor site offers the poorest prognosis of the immune subpopulations, a multivariable analysis was performed to determine the independent prognostic impact of TIME clusters on OS in the entire cohort (Figure 4).
Only patients with complete clinical data w ere included in the multivariable analysis. Notably, tumor mutational burden and tumor content were similar across clusters, and cluster 1 demonstrated few RAS mutations and a decreased microsatellite instability score when compared to the other clusters. Full visualization of this data can be seen in Table 2.

Discussion:
With the prevalence of melanoma continuing to rise and the persistence of a subgroup of patients that suffers poor prognosis despite appropriate therapies, it is of increasing importance that novel predictors of therapeutic response and therapeutic targets are developed. In this analysis, we explore the landscape of the TIME in melanoma, and identify unique clusters based on the prevalence of lymphocytes and monocytes within the tumor samples. We demonstrate that a low lymphocyte-to-monocyte (L:M) ratio in the locoregional tumor specimen with high M0-macrophage enrichment confers worse prognosis in SKCM. Additionally, this group displays fewer predicted responders to immunotherapy, concordant with results presented from analyses involving the peripheral blood ratios [24]; poorly prognostic clinical characteristics (higher tumor mutational burden and necrosis, lower lymphocyte score, higher percentage of BRAF mutants), and higher rates of metastatic disease and recurrence. Together, these results suggest that a large undifferentiated macrophage pool in the setting of low lymphocytic infiltrate in the locoregional tumor site may serve to create a favorable environment for tumor progression. It is also possible that undifferentiated macrophages exert an immunosuppressive effect on the tumor, leading to poor lymphocytic infiltration, and thus poorer OS.
Peripheral blood L:M ratio was first found to be a prognostic factor in hematological malignancies [33,34]. Subsequently, a higher L:M ratio has been shown to be associated with improved OS in over a dozen different solid tumors [35].  [36]. TILs are thought to be responsible for cellular as well as humoral antitumor immune responses that contribute to tumor control. Indeed, high numbers of TILs are as sociated with improved clinical outcomes [37][38][39]. In addition, lymphopenia was previously found correlated to overall survival in prospectively collected series of patients with metastatic breast cancer, non-Hodgkin lymphoma, and soft tissue sarcoma [40]. A low lymphocyte count might result in an inadequate immune response in the control of tumor, which may help to explain why a low L:M ratio correlated with poorer OS and increased rates of metastasis, necrosis, and Breslow depth at diagnosis.
TAMs are regarded as key contributors to the crosstalk between tumor and stromal cells, orchestrating key events necessary for cancer progression including skewing adaptive responses, cell growth, angiogenesis, and extracellular matrix remodeling -changes which all lead to a premetastatic niche [41,42]. Further sub-classification of TAMs is necessary as their polarization influences their behavior. At a basic level, macrophages are separated into the M1 subtype which is pro-inflammatory, anti-fibrotic and activated by LPS, TNF, and IFN-Y and the M2 subtype which is anti-inflammatory, pro-fibrotic, and stimulated by IL4 and IL13 [43,44]. Given the dynamic nature of the tumor microenvironment and the numerous stimuli within it [43], emerging classification paradigms describe TAMs on a continuum of many subtypes or a mixed phenotype that is consistent with neither M1 nor M2 phenotypes [45]. Regardless of the phenotype, all TAMs participate in some degree of immunosuppression [46].
In ovarian cancer and glioblastoma, transcriptomic profiling demonstrated that M0 macrophages do not fit into the canonical M1 or M2 model, but M0 macrophages did have high expression of M2 markers, and a transcriptional profile more similar to M2 macropha ges [47,48]. Ultimately, M0s may represent another type of TAM or an incompletely differentiated M2 [47]. M0 macrophages were found to be one of the cell subsets most strongly associated with poor outcome in breast cancer [49], prostate cancer [50], and lung adenocarcinoma [51], while reduced M0 content has been associated with better prognosis in bladder cancer [52]. In a comprehensive analysis of digestive system cancers, M0 macrophages were among the most prevalent immune cell fractions, with M0 enriched clusters associated with decreased recurrence-free survival (RFS) and worse prognostic immune score [53]. It is possible that the presence of these tumor -promoting cells offered more prognostic significance in the locoregional tumor sample in our study due to their promotion of immunosuppression leading to aggressive phenotype and a pre-metastatic niche, which translated to poorer survival. In patients who have already metastasized, prognosis will likely already be poorer, and the relative intratumoral immune cell concentrations at these sites thereby offers little in the way of stratifying prognosis. With the advent of pembrolizumab, nivolumab, and ipilimumab, the outlook of treatment of advanced melanoma has improved significantly. Patients treated with pembrolizumab in the KEYNOTE-001 study were found to have a median PFS of 4 months and a median OS of 23 months [54]. However, these medications fail to induce a response in a subset of patients, they can cause immune-related adverse events, and they are quite expensive. Therefore, finding biomarkers to help predict which patients are more likely to benefit from treatment is an important area of investigation. Our study found that the cluster of patients with a decreased L:M ratio and M0macrophage enrichment demonstrated worse OS in the locoregional tumor and in a multivariable analysis accounting for other poor clinical prognostic factors, such as stage, Breslow depth, and BRAF status. Monocytes are recruited into tumors and promote tumor progression [55].
Interleukin-10 is an immunosuppressive cytokine produced mainly by monocytes. In metastatic melanoma, high interleukin-10 levels were correlated with worse survival [56]. Treatment with ipilimumab, nivolumab, or an ipilimumab/nivolumab combination in patients caused significant changes in gene expression in CD3+ T cells, but relatively fewer changes in monocytes [57]. These properties of monocytes may explain our study's finding of why patients with higher baseline M0 macrophage content (and therefore lower L:M ratio) demonstrated poor prognosis and fewer predicted responders to immunotherapy.
Our analysis is a snapshot in time -reflecting when the tumor was resected and sequenced. As a result, the dynamic influences on macrophage polarization and the changing tumor microenvironment were not captured. Additionally, bulk tumor specimen analysis does not capture the immune contexture that is critical to macrophage behavior. Single cell approaches may address some of these issues, however they have the same temporal limitation, are subject to a bias towards more highly expressed genes, they require optimally preserved clinical specimens, and their high cost limits profiling of large numbers of patients [58]. For these reasons, in a clinical decisionmaking context, an exclusively single cell approach is not feasible. Thus, it is important to use single cell approaches to augment bulk tumor profiling from databases such as TCGA by validating findings from larger scale analyses of bulk specimens. Although we acknowledge that the scope of this study is limited to transcriptome data and that orthogonal studies of corresponding protein expression patterns were not feasible, transcriptome data has become increasingly relevant in the era of molecular medicine. Finally, it will be interesting to examine the significance of the L:M Low , Monocyte High , M0 High cluster in a prospective manner based upon directly measured patient responses to immunotherapy.

Conclusion:
In conclusion, our study has built on the previous literature by characterizing immune clusters within SKCM. With this study we characterize the immune microenvironment landscape of melanoma as it relates to intratumoral lymphocyte and macropha ge concentrations, and thereby define a subset of patients with melanoma that display poorer overall outcomes, including decreased OS and fewer ICB responders, based on locoregional tumor biopsy findings. This subset of patients may benefit from more aggressive treatment earlier in the disease course. Additionally, we demonstrate a technique to conduct a comprehensive bioinformatical analysis using publicly available whole transcriptome sequencing data to characterize the tumor immune   Kaplan-Meier curve demonstrating survival difference between clusters based on collection from distant metastatic or regional lymph node site. Panel B. Kaplan -Meier curve demonstrating survival difference between clusters based on collection from primary tumor or regional skin or soft tissue site.